| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithm Development in Materials Science and Engineering
|
| Presentation Title |
High Speed Artificial Neural Network Implementation of Interatomic Force Fields in Metals |
| Author(s) |
Doyl Dickel, Christopher Barrett, Mashroor Nitol |
| On-Site Speaker (Planned) |
Doyl Dickel |
| Abstract Scope |
Machine learning techniques, particularly artificial neural networks (ANNs), have proven to be effective at reproducing DFT and first-principles calculations at accelerated timescales. These tools are capable of sub meV/atom accuracy while operating scaling linearly with the size of the system. However, many of the popular implementations are still orders of magnitude slower than traditional forcefield models such as EAM and MEAM. Overcoming this performance gap is essential to the production of ANNs which are useful to solve modern molecular dynamics problems. Here, we demonstrate our ANN formalism, inspired by existing semi-empirical methods. It is shown that using a physically motivated fingerprint and other innovations from classical methods, the computation time of these force fields can rival MEAM. For several metals, traditionally difficult to model at the atomic scale, we demonstrate the ability of this formalism to produce force fields that can lead to new physical insights. |
| Proceedings Inclusion? |
Planned: |